How to extract keywords from HubSpot Sales Sales Emails using generative AI

Keyword Identification

HubSpot Sales

As a sales professional, it's vital to understand your customer's needs and tailor your communication accordingly. However, going through each email conversation can be time-consuming and challenging. Fortunately, there is a way to identify essential insights from your HubSpot sales emails using generative AI. In this post, we'll show you how to extract keywords from your HubSpot sales emails using natural language processing (NLP) techniques.

What is Keyword Extraction?

Keyword extraction involves identifying the most important or relevant words or phrases in a piece of text. It can help you extract key information and themes from text, including search engine optimization (SEO), content analysis, and topic modeling.

Keyword extraction can be performed using machine learning algorithms that learn to recognize patterns and features in the text associated with important words or phrases. These algorithms can be trained on a labeled dataset of text to identify the most relevant keywords.

You can use keyword extraction to quickly analyze and summarize large amounts of text data to identify the most important information and themes.

Example Use Cases

Use cases for extracting keywords from HubSpot sales emails include:

Identifying the most common customer pain points
Pinpointing key features or benefits that resonate with customers
Creating targeted marketing campaigns
Improving sales team training and performance
Personalizing communication with customers

Teams that might find these use cases helpful include: sales, marketing, customer support, product, and operations.

Accessing your HubSpot Sales Emails and Identifying Preliminary Keywords

To extract keywords from your HubSpot sales emails, you'll first need to identify the data you want to work with. You can access your HubSpot sales emails by exporting them in CSV format from HubSpot or by querying them from your data warehouse or BI tool.

Once you have your data, it can be helpful (but not necessary) to identify common keywords that you may want to extract from your sales emails. You can use generative AI tools to identify and measure the frequency of keywords and suggest additional keywords you may not have been aware of.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your HubSpot sales emails. This will help you identify the most important information and themes in your sales emails, allowing you to tailor your communication more effectively.

Conclusion

Keyword extraction using generative AI can provide valuable insights from your HubSpot sales emails, allowing you to better understand your customer's needs and tailor your communication accordingly. By using NLP techniques, you can quickly analyze and summarize large amounts of text data, providing you with a competitive edge in the market.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

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